生物信息学中很牛逼的一篇论文
High-throughput messenger RNA (mRNA) expression profiling with microarray has been demonstrated
as a more effective method of cancer diagnosis and treatment than the traditional morphology or clinical
parameter based methods. Recently, the discovery of a category of small non-coding RNAs, named
microRNAs (miRNAs), provides another promising method of cancer classification. miRNAs play a critical
role in the tumorigenic process by functioning either as oncogenes or as tumor suppressors. Here, we
apply a neural based classifier, Default ARTMAP, to classify broad types of cancers based on their miRNA
expression fingerprints. As the miRNA expression data usually have high dimensionalities, particle swarm
optimization (PSO) is used for selecting important miRNAs that contribute to the discrimination of
different cancer types. Experimental results on the multiple human cancers show that Default ARTMAP
performs consistently well on all the data, and the classification accuracy is better than or comparable to
that of the other popular classifiers. Also, the selection of informative miRNAs can further improve the
performance of classifiers and provide meaningful insights into cancer researchers.
